Gaze Restriction and Reactivation of Place-bound Content Drive Eye Movements During Mental Imagery
Bibliographic record
Abstract
When we imagine a picture, we move our eyes even though the picture is physically not present. These eye movements provide information about the ongoing process of mental imagery. Eye movements unfold over time, and previous research has shown that the temporal gaze dynamics of eye movements in mental imagery have unique properties, which are unrelated to those in perception. In mental imagery, refixations of previously fixated locations happen more often and in a more systematic manner than in perception. The origin of these unique properties remains unclear. We tested how the temporal structure of eye movements is influenced by the complexity of the mental image. Participants briefly saw and then maintained a pattern stimulus, consisting of one (easy condition) to four black segments (most difficult condition). When maintaining a simple pattern in imagery, participants restricted their gaze to a narrow area, and for more complex stimuli, eye movements were more spread out to distant areas. At the same time, fewer refixations were made in imagery when the stimuli were complex. The results show that refixations depend on the imagined content. While fixations of stimulus-related areas reflect the so-called 'looking at nothing' effect, gaze restriction emphasizes differences between mental imagery and perception.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".